MNIST [1]} that contains 60000 training samples and 10,000 testing samples with ten balanced classes. Each grayscale sample is stored as a \(28\times 28\)  matrix. The classification model used in our experiments consists of two convolutional layers and two fully connected layers. Following each convolutional layer is a max-pooling layer with a pooling size of \(2\times 2\) . For activations, we use ReLU and for classification, we use softmax. Cross-entropy loss is used.
